What type of study is this?

This is a Quantitative Study study.

October 11, 2025Journal of ForecastingOpen Access

Modeling and Forecasting Stochastic Seasonality: Are Seasonal Autoregressive Integrated Moving Average Models Always the Best Choice?

View Full Paper

Ask AI

Bookmark

Authors

This analysis compares forecasting abilities of SARIMA and SARUMA models for stochastic seasonality, suggesting SARUMA may perform better.

SARUMA models provide better control of seasonal patterns compared to SARIMA and near-nonstationary ARMA models.
The performance of SARIMA models deteriorates significantly when over-differencing occurs, inflating forecast error variance.
Theoretical calculations and simulations indicate SARUMA models outperform when using the HEGY test to determine roots.
Regularity of seasonal patterns in SARUMA and SARIMA models showcases increasing amplitudes and variability in the medium run.

Ioannidis et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

This analysis compares forecasting abilities of SARIMA and SARUMA models for stochastic seasonality, suggesting SARUMA may perform better.